Journal
FOOD CHEMISTRY
Volume 221, Issue -, Pages 311-316Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2016.10.068
Keywords
Green tea; Longjing tea; Metabolomics analysis; UPLC-Q-TOF/MS; Sensory evaluation; Tea quality; PLS regression
Funding
- Special Fund for Agro-scientific Research in the Public Interest of the Ministry of Agriculture of China [201203046]
- Chinese Academy of Agricultural Sciences through the Agricultural Sciences and Technology Innovation Project of China [CAAS-ASTIP-2014-TRICAAS]
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Metabolomics profiling provides comprehensive picture of the chemical composition in teas therefore may be used to assess tea quality objectively and reliably. In the present experiment, water and methanol extracts of green teas from China were analyzed by ultra-performance liquid chromatography-quadru pole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) with the objectives to establish a model for quality prediction and to identify potential marker metabolites. The blindly evaluated sensory score of green teas was predicted with excellent power (R-2 = 0.87 and Q(2) = 0.82) and accuracy (RMSEP = 1.36) by a partial least-squares (PLS) regression model based on water extract. By contrast, methanol extract failed to reasonably predict the sensory scores. The levels in water extract of neotheaflavin, neotheaflavin 3-O-gallate, trigalloyl-beta-D-glucopyranose, myricetin 3,3'-digalactoside, catechin-(4 alpha -> 8)-epigallocatechin and kaempferol were significantly larger whereas those of theogallin and gallocatechin were less in the low (score < 87) than in the high score (>= 90) group. (C) 2016 Elsevier Ltd. All rights reserved.
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